# Making Video Quality Assessment Models Robust to Bit Depth

arXiv: https://arxiv.org/abs/2304.13092 · HTML: https://mlsanity.com/paper/2304.13092/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 25 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [Quality Assessment of In-the-Wild Videos](https://mlsanity.com/paper/1908.00375/) (2019)
- [UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content](https://mlsanity.com/paper/2005.14354/) (2020)
- [RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content](https://mlsanity.com/paper/2101.10955/) (2021)
- [ChipQA: No-Reference Video Quality Prediction via Space-Time Chips](https://mlsanity.com/paper/2109.08726/) (2021)
- [A Subjective and Objective Study of Space-Time Subsampled Video Quality](https://mlsanity.com/paper/2102.00088/) (2021)
- [Subjective Assessment of High Dynamic Range Videos Under Different Ambient Conditions](https://mlsanity.com/paper/2209.10005/) (2022)

## Built on by (most cited first, 6 of 6)

- [Video Quality Assessment: A Comprehensive Survey](https://mlsanity.com/paper/2412.04508/) (2024)
- [HIDRO-VQA: High Dynamic Range Oracle for Video Quality Assessment](https://mlsanity.com/paper/2311.11059/) (2023)
- [A FUNQUE Approach to the Quality Assessment of Compressed HDR Videos](https://mlsanity.com/paper/2312.08524/) (2023)
- [Subjective Quality Assessment of Compressed Tone-Mapped High Dynamic Range Videos](https://mlsanity.com/paper/2403.15061/) (2024)
- [Seeing Beyond 8bits: Subjective and Objective Quality Assessment of HDR-UGC Videos](https://mlsanity.com/paper/2603.00938/) (2026)
- [Cut-FUNQUE: An Objective Quality Model for Compressed Tone-Mapped High Dynamic Range Videos](https://mlsanity.com/paper/2404.13452/) (2024)

## Abstract (arXiv)

We introduce a novel feature set, which we call HDRMAX features, that when included into Video Quality Assessment (VQA) algorithms designed for Standard Dynamic Range (SDR) videos, sensitizes them to distortions of High Dynamic Range (HDR) videos that are inadequately accounted for by these algorithms. While these features are not specific to HDR, and also augment the equality prediction performances of VQA models on SDR content, they are especially effective on HDR. HDRMAX features modify powerful priors drawn from Natural Video Statistics (NVS) models by enhancing their measurability where they visually impact the brightest and darkest local portions of videos, thereby capturing distortions that are often poorly accounted for by existing VQA models. As a demonstration of the efficacy of our approach, we show that, while current state-of-the-art VQA models perform poorly on 10-bit HDR databases, their performances are greatly improved by the inclusion of HDRMAX features when tested on HDR and 10-bit distorted videos.